[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126213-en":3,"doc-seo-126213-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126213,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Fusion of Opportunistic Networks with Machine Learning: Present and Future","Opportunistic Networks (OppNets) rely on intermittent connectivity, where an end-to-end path between source and destination is rarely available. Nodes therefore use store-carry-forward forwarding to store messages, carry them while moving, and forward only when contacts arise, enabling delivery in infrastructure-limited settings such as disaster or remote areas but at the cost of high delays and uncertainty. Machine Learning (ML) is used to exploit mobility, contact frequency, and contextual patterns, improving delivery rate, delay, and decision-making. This survey consolidates state-of-the-art ML-OppNet integration and highlights future directions, including data sparsity, device computational limits, privacy/security risks, and the need for realistic testing.","Metallurgical and Materials Engineering Research paper  \nFusion of Opportunistic Networks with Machine Learning:  \nPresent and Future  \nSonal Beniwal1, Puneet Garg2, Rakesh Rajpal3, Neetu Sharma4, Harish Kumar Mittal5  \n1Associate Professor, BPSMV, Khanpur Kalan, Gohana, Sonipat, Haryana, India 2Associate Professor, KIET Group of Institutions, Delhi NCR, Ghaziabad, India 3Professor, SAITM, Gurugram Delhi NCR, Haryana, India 4Professor, Galgotias University Greater Noida, Uttar Pradesh, India 5Professor, BMIET, Sonepat, Delhi NCR, India  \n[sonalkharb@gmail.com](sonalkharb@gmail.com1)[1](sonalkharb@gmail.com1), [puneetgarg.er@gmail.com](puneetgarg.er@gmail.com2)[2](puneetgarg.er@gmail.com2), [rajpal.rakesh@gmail.com](rajpal.rakesh@gmail.com3)[3](rajpal.rakesh@gmail.com3),  \n[neetush75@gmail.com](neetush75@gmail.com4)[4](neetush75@gmail.com4), [mittalberi@gmail.com](mittalberi@gmail.com5)[5](mittalberi@gmail.com5)  \nAbstract: Opportunistic Networks (OppNets) are mobile ad hoc networks characterized by intermittent connectivity and the lack of a guaranteed end-toend path between source and destination. Nodes in an OppNet employ a storecarry-forward strategy – messages are stored and carried by mobile nodes until a communication opportunity arises, at which point they are forwarded. This paradigm enables data delivery in challenging environments (disaster areas, remote regions, etc.) where conventional infrastructure is absent, but it also introduces high delays and uncertainty. Machine Learning (ML) has emerged asa powerful tool to improve OppNet performance by exploiting patterns in node mobility, contact frequency, and context. This paper provides an extensive survey of the state-of-the-art in merging ML with OppNets and discusses future developments. In this paper, we analyze how ML algorithms have enhanced message delivery rates, reduced delays, and improved decision-making in OppNets (often outperforming traditional protocols by significant margins), as illustrated by recent results in the literature. Key challenges at this fusion include data sparsity, computational constraints on mobile devices, privacy/security concerns, and the need for realistic testing.  \nKeywords: Machine Learning, Opportunistic Networks, OppNet  \n1. Introduction  \nOpportunistic Networks (OppNets) are a class of delay-tolerant wireless networks in which mobile nodes communicate opportunistically – i.e. only when they happen to come into contact – rather than over stable end-to-end links. In an OppNet, a contemporaneous multi-hop path rarely exists between a given source and destination. Instead, data are transferred through a series of sporadic contacts, as nodes store messages, carry them while moving, and forward them upon encountering new nodes [23][27] .  \nThis store-carry-forward mechanism, illustrated conceptually in Figure 1, allows OppNets to deliver messages despite frequent disconnections and topology changes. Common examples of OppNets include mobile social networks formed by pedestrians or vehicles, Delay-Tolerant Networks for wildlife tracking or rural communications, and sensor data collection networks using mobile sinks. In all such cases, devices must tolerate delays and uncertainty; for instance, a sensor reading might be relayed through multiple opportunistic hops over hours or days to reach a data center [20][21] .  \nFigure 1: Example decision flow in an ML-enhanced OppNet routing strategy. A node forwards a message only if certain learned conditions are met (e.g. the next hop is a socially connected, non-selfish node with sufficient resources); otherwise it waits for a better opportunity. Such intelligent forwarding decisions, often obtained via machine learning models, can greatly improve delivery outcomes in intermittently connected networks.  \nDespite their fragmented connectivity, OppNets are significant because they enable communication in scenarios where conventional networks fail. By leveraging node mobility and peer-to-peer ","cbCaifGt63Stn0vr","https://ap.wps.com/l/cbCaifGt63Stn0vr","pdf",311397,1,9,"English","en",105,"# Introduction\n## Opportunistic Networks and store-carry-forward\n## ML-enhanced routing decision flow\n# Background\n## Opportunistic Networks","[{\"question\":\"What makes Opportunistic Networks (OppNets) different from conventional networks?\",\"answer\":\"OppNets experience intermittent connectivity and typically do not maintain a contemporaneous end-to-end path between source and destination. Communication happens through sporadic contacts as nodes move and encounter each other.\"},{\"question\":\"How does the store-carry-forward mechanism work in an OppNet?\",\"answer\":\"Nodes store incoming messages, carry them while they move, and forward the messages when a suitable communication opportunity arises with another node.\"},{\"question\":\"How can machine learning improve OppNet routing outcomes?\",\"answer\":\"ML can learn from patterns in mobility, contact frequency, and context to make forwarding decisions that increase message delivery rates and reduce delays compared with traditional protocols.\"}]","Fusion of Opportunistic Networks with Machine Learning: Present and Future | PDF",1785903818,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"fusion-of-opportunistic-networks-with-machine-learning-present-and-future","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/fusion-of-opportunistic-networks-with-machine-learning-present-and-future/126213/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What makes Opportunistic Networks (OppNets) different from conventional networks?","Question",{"text":76,"@type":77},"OppNets experience intermittent connectivity and typically do not maintain a contemporaneous end-to-end path between source and destination. Communication happens through sporadic contacts as nodes move and encounter each other.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the store-carry-forward mechanism work in an OppNet?",{"text":81,"@type":77},"Nodes store incoming messages, carry them while they move, and forward the messages when a suitable communication opportunity arises with another node.",{"name":83,"@type":74,"acceptedAnswer":84},"How can machine learning improve OppNet routing outcomes?",{"text":85,"@type":77},"ML can learn from patterns in mobility, contact frequency, and context to make forwarding decisions that increase message delivery rates and reduce delays compared with traditional protocols.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]